

'''simple docstring'''
from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

_lowerCamelCase      					=     TypeVar("""T""")
class 					_snake_case		(Generic[T]):






			def __init__(  self			,_snake_case			,_snake_case     ):
				UpperCAmelCase_     :      Union[str, Any]						=  None

				UpperCAmelCase_     :      str						=  len(SCREAMING_SNAKE_CASE_     )
				UpperCAmelCase_     :      Dict						=  [any_type for _ in range(self.N     )] + arr
				UpperCAmelCase_     :      Optional[Any]						=  fnc
				self.build()






			def 		UpperCamelCase__							(  self     ):


				for p in range(self.N - 1			,0			,-1     ):
					UpperCAmelCase_     :      Dict						=  self.fn(self.st[p * 2]			,self.st[p * 2 + 1]     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case     ):
				p += self.N
				UpperCAmelCase_     :      List[Any]						=  v
				while p > 1:
					UpperCAmelCase_     :      Optional[Any]						=  p // 2
					UpperCAmelCase_     :      Optional[int]						=  self.fn(self.st[p * 2]			,self.st[p * 2 + 1]     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case     ):  # noqa: E741
				UpperCAmelCase_					,					UpperCAmelCase_     :      List[Any]						=  l + self.N, r + self.N

				UpperCAmelCase_     :      Optional[int]						=  None
				while l <= r:
					if l % 2 == 1:
						UpperCAmelCase_     :      Optional[int]						=  self.st[l] if res is None else self.fn(SCREAMING_SNAKE_CASE_			,self.st[l]     )
					if r % 2 == 0:
						UpperCAmelCase_     :      int						=  self.st[r] if res is None else self.fn(SCREAMING_SNAKE_CASE_			,self.st[r]     )
					UpperCAmelCase_					,					UpperCAmelCase_     :      Dict						=  (l + 1) // 2, (r - 1) // 2
				return res


if __name__ == "__main__":
					from functools import reduce

					_lowerCamelCase      					=     [1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

					_lowerCamelCase      					=     {
					    0: 7,
					    1: 2,
					    2: 6,
					    3: -14,
					    4: 5,
					    5: 4,
					    6: 7,
					    7: -10,
					    8: 9,
					    9: 10,
					    10: 12,
					    11: 1,
					}

					_lowerCamelCase      					=     SegmentTree(test_array, min)
					_lowerCamelCase      					=     SegmentTree(test_array, max)
					_lowerCamelCase      					=     SegmentTree(test_array, lambda a, b: a + b)


					def  a__       (    )     ->					None:


						"""simple docstring"""





						for i in range(len(_lowerCamelCase				)				):
							for j in range(_lowerCamelCase	,				len(_lowerCamelCase				)				):
								UpperCAmelCase_     :      List[Any]						=  reduce(_lowerCamelCase	,				test_array[i : j + 1]				)
								UpperCAmelCase_     :      Any						=  reduce(_lowerCamelCase	,				test_array[i : j + 1]				)
								UpperCAmelCase_     :      Optional[Any]						=  reduce(lambda _SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				: a + b	,				test_array[i : j + 1]				)
								assert min_range == min_segment_tree.query(_lowerCamelCase	,				_lowerCamelCase				)
								assert max_range == max_segment_tree.query(_lowerCamelCase	,				_lowerCamelCase				)
								assert sum_range == sum_segment_tree.query(_lowerCamelCase	,				_lowerCamelCase				)

					test_all_segments()

					for index, value in test_updates.items():
										_lowerCamelCase      					=     value
										min_segment_tree.update(index, value)
										max_segment_tree.update(index, value)
										sum_segment_tree.update(index, value)
										test_all_segments()






"""simple docstring"""






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class 			lowercase					( lowercase__		):
			lowercase        =     ['''image_processor''', '''tokenizer''']
			lowercase        =     '''CLIPImageProcessor'''
			lowercase        =     ('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')






			def __init__(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	int=None      ,SCREAMING_SNAKE_CASE_  :	str=None      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  None
				if "feature_extractor" in kwargs:
					warnings.warn(
					    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
					    ''' instead.'''      ,SCREAMING_SNAKE_CASE_      ,)
					lowerCAmelCase												=  kwargs.pop('''feature_extractor'''			)

				lowerCAmelCase												=  image_processor if image_processor is not None else feature_extractor
				if image_processor is None:
					raise ValueError('''You need to specify an `image_processor`.'''			)
				if tokenizer is None:
					raise ValueError('''You need to specify a `tokenizer`.'''			)

				super().__init__(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)






			def __call__(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	List[str]=None      ,SCREAMING_SNAKE_CASE_  :	Optional[int]=None      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=None      ,**SCREAMING_SNAKE_CASE_  :	Any			)       ->	List[Any]:





				"""simple docstring"""


				if text is None and images is None:
					raise ValueError('''You have to specify either text or images. Both cannot be none.'''			)

				if text is not None:
					lowerCAmelCase												=  self.tokenizer(SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)

				if images is not None:
					lowerCAmelCase												=  self.image_processor(SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)

				if text is not None and images is not None:
					lowerCAmelCase												=  image_features.pixel_values
					return encoding
				elif text is not None:
					return encoding
				else:
					return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_			)      ,tensor_type=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	List[str]      ,*SCREAMING_SNAKE_CASE_  :	Union[str, Any]      ,**SCREAMING_SNAKE_CASE_  :	Tuple			)       ->	Optional[Any]:





				"""simple docstring"""


				return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Union[str, Any]      ,*SCREAMING_SNAKE_CASE_  :	int      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	Any:





				"""simple docstring"""


				return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			@property
			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  self.tokenizer.model_input_names
				lowerCAmelCase												=  self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)


